Following severe flooding in Hull, UK in 2007, the Association of British Insurers asked the UK Environment Agency to improve flood modelling for heavy rainfall in towns. We took up the challenge to produce a robust pluvial (rainfall) modelling test to set a best practice standard on modelling inputs.

The Challenge

The test set out to determine which set of inputs gave the most accurate predictions for urban pluvial flood modelling. These included a variety of topographic surface representations and topographical resolutions, as well as different approaches to the use of building information within models.

Our Solution

We created a variety of Digital Terrain Models (DTM) and Digital Surface Models (DSM). These included:

  • DSMs – buildings, trees and other features
  • ‘Bald earth’ DTMs with buildings removed
  • DTMs with additional building data like heights and roof features, but not vegetation

Each of these were then resampled to different horizontal grid resolutions (2m, 3m, 4m and 5m for LiDAR) to understand resolution effects. Hydrological data was based on gauge readings from the University of Hull which showed over 110mm of sustained rainfall with rates of over 6mm/hour. This was estimated to be more than a 150-year return period based on the Centre for Ecology and Hydrology (CEH) flood estimation handbook.


To enable binary classification of a building’s flood status, we extracted building-level information from modelled flood depth grids. This was done using GIS (geographic information system) analysis and comparison with observed flood data. We determined:

  • Correct wet predictions: buildings correctly predicted to flood
  • Correct dry predictions: buildings correctly predicted not to flood
  • False wet predictions: buildings predicted to flood which didn’t flood
  • False dry predictions: buildings predicted not to flood which did flood

We assessed the predictive ability of each simulation and penalised over and under-prediction of flooded buildings. We also used the Kappa statistic to identify those model simulations which could better predict flooded and non-flooded buildings (as opposed to those which were correct due to chance).

Key findings

Perhaps surprisingly, DSMs performed less well than either of the DTM types. We discovered that including building data actually reduced the models’ effectiveness and that this was primarily due to the overprediction of flooding caused by artefacts.

Of the DTM approaches, simulations based on DTM plus buildings slightly underperformed ‘bald earth’ DTM. This was due to the effect that adding detailed building information has upon floodplain storage.

The Outcomes

Our research led to the establishment of several best practices to improve urban pluvial modelling in Hull and elsewhere. To ensure optimum predictions of the likelihood of individual buildings flooding in urban pluvial situations, these inputs should be used:

  • A LiDAR (Light Detection and Ranging) DTM using a ‘bald earth’ approach
  • When using DTM rather than DSM, a grid resolution of 5m was sufficient
  • DSM data requires a grid resolution of 3m
  • Wherever possible, validation as well as pluvial flood modelling should be carried out at the building scale


The image shows 100-year Fluvial Flood Map for the Hull area

2007 saw Britain’s wettest May-July since records began in 1776.

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